Contrasting patterns of nitrogen release from fine roots and leaves driven by microbial communities during decomposition

Abstract

Leachate from decaying root and leaf litter plays crucial roles in soil biogeochemical processes in forest ecosystems. Unlike for leaf litter, however, the chemical composition and microbial community of root litter leachate are poorly understood. We hypothesized that both leachate nitrogen (N) composition and microbial communities differ between plant organs and decomposition stages and that leachate composition affects microbial community composition. We conducted a 2.5-year laboratory incubation using root and leaf substrate from Cryptomeria japonica and Chamaecyparis obtusa. We monitored the N forms released and used metabarcoding to characterize the microbial communities. Leachate N accounted for 40 % and 30 % of net N losses from C. japonica and C. obtusa roots, respectively; the remainder was probably lost in gaseous forms. In contrast, leaves absorbed N during the incubation regardless of tree species. The predominant N form in root leachate was nitrate (NO3−); cumulative NO3− quantity was 22.6 and 25.5 times greater in root than in leaf leachate for C. japonica and C. obtusa, respectively. A nitrifying bacterium was selected as the indicator taxon in root substrates, whereas many families of N-fixing bacteria were selected in leaf substrates. At the end of the incubation period, bacterial taxonomic diversity was high in both organs from both tree species, ranging from 177 to 339 taxa and increasing with time. However, fungal diversity was low for both organs (72 to 155 taxa). Shifts in bacterial community structure were related to NO3− concentration and leachate pH, whereas shifts in fungal community structure were related to leachate pH. These results suggest that the contrasting N dynamics of root and leaf substrates are strongly affected by the characteristics of and the microbes recruited by their leachates. Understanding organ-specific litter N dynamics is indispensable for predicting N cycling for optimal management of forest ecosystems in a changing world.